System and method for precision detection of biomarkers

ABSTRACT

A method for detecting biomarkers with shortened test time and enhanced precision is provided. A sample from the body fluid is made to flow over a sensor surface coated with a capture antibody to allow binding of a biomarker in the sample to the capture body. An optical method detects and counts the individual binding events along the sensor surface with single molecule resolution, and difference in the binding events along the sensor surface is detected in real-time and analyzed to determine the biomarker concentration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. Pat. Application No. 16/689,530filed on Nov. 20, 2019, which claims priority to U.S. Provisional Pat.Application No. 62/770,056 filed on Nov. 20, 2018, wherein the entirecontents of the foregoing applications are hereby incorporated byreference herein.

TECHNICAL FIELD

The method and system disclosed herein relates to rapid detection andprecise quantification of molecular biomarkers in human blood and inother types of body fluids. The intended application of the method andsystem disclosed herein includes timely detection of acute diseases,including heart disease and sepsis, as well as monitoring of the diseaseprogression.

BACKGROUND

Detection, identification and quantification of biomarkers, such asproteins, peptides, exosomes, hormones, neurotransmitters, metabolitesand nucleic acids, are critical to disease diagnosis and progressionmonitoring ^([1,2]). Various approaches have been developed, but themost well-established strategy is to use antibodies. An antibody canrecognize and bind to a biomarker (antigen) specifically, and thebinding event is then converted into a readable optical, electronic,mechanical, or magnetic signal. A well-known device is lateral flowimmunoassay using paper-based strips. These strips are based onvisualization of human eyes, which are limited for non-quantitativeapplications, and insensitive for many clinical needs. To detect lowconcentration biomarkers, ELISA (enzyme-linked immunosorbent assay) hasbeen developed, which amplifies antibody-biomarker binding via enzymaticreactions and converts reaction products into an optical signal (e.g.,color changes). Although ELISA is the most established and powerfultechnique used in clinical and research labs, its limit of detection(LOD) and time required for testing are often insufficient for manyclinical applications. The test time referred here in this disclosure isthe time duration from the introduction of the sample into the system toreporting of the test results by the system.

Recent technological advances have made it possible to detect singlemolecules, which have been used to measure the binding of a singlebiomarker molecule to a capture antibody with improved LOD and shortentest time. This single-molecule approach is referred to as digitalimmunoassay, of which one example is digital ELISA. To date, threeplatforms have been proposed to achieve digital immunoassay. They allrely on the antibody binding to a biomarker molecule but differ in theplatform used to detect the biomarker-antibody binding events.

The first platform is to replace the large reaction well in thetraditional ELISA with an array of microwells ^([3,4]). The volume ofthe microwells is small, such that each microwell has either nobiomarker present or a single biomarker molecule that binds to captureantibody conjugated with a magnetic bead in the microwell. In the formercase, no enzymatic reaction takes place, and fluorescence readout fromthe microwell is null (or background level). In the latter case,however, the enzymatic reaction products lead to fluorescence emission,which is detected as a binding event.

The second digital immunoassay platform combines single moleculefluorescent detection with flow cytometry ^([5]). A sample solutioncontaining a biomarker is incubated with a capture antibody attachedeither to a plate or a bead. This will allow the binding of thebiomarker to the capture antibody. After the incubation, a buffersolution is introduced to wash away unbound biomarkers. A second(detection) antibody conjugated to an alexa fluor tag is then added tothe sample for further incubation, followed by second washing. Aneutralization buffer is added to break the detection antibody-biomarkercomplex from plate or bead, which flows through a narrow capillarycrossing a laser beam to emit a fluorescent signal detected with aphotodetector.

The third platform uses metal nanoparticles (e.g., gold) as a signalreadout mechanism ^([6-8]). The nanoparticles bind to the biomarkerscaptured on a sensor surface and detected individually via dark field,interference and plasmonic optical imaging techniques. Using goldnanorods (GNRs) it has been shown that the polarization of the GNRsprovides additional information that help detection of molecularbinding.^([6])

These digital immunoassay technologies provide single molecule detectioncapability, which significantly improves the LOD of the traditionalELISA. However, the improvement is often achieved at the expense of testtime. Another more important remaining drawback of these digitalimmunoassay technologies is the lack pf precision. Fast and precisedetection are crucial for disease diagnosis and treatment ^([9]).

An important example is cardiovascular diseases, where timely andprecise assessment of the patient condition is often a matter of life ordeath ^([9,10]). A well-established biomarker in cardiovascular diseaseis troponin(s), which is a complex of three regulatory proteinsincluding troponin C (TnC), troponin I (Tnl) and troponin T (TnT). Thetroponin complex (e.g., Troponin T) concentration in human blood variesover an extremely broad range, from as low as a few ng/L in healthypopulation to as high as 10⁴ ng/L ^([11]) in patients withcardiovascular disease. This requires a detection technology not onlywith LOD but also with wide dynamic range. In the case of cardiovasculardiseases, monitoring troponin level of a patient with a time interval of10-20 mins or shorter is highly desired for the doctor to diagnose thecondition, assess the disease progression and evaluate the outcome ofmedication. This requires a fast and precise detection technology. Highprecision is needed because the change in the troponin level of thepatient can be rather small, and thus difficult to resolve withconventional detection technologies^([11]). The current technologiesfall into two categories. One category is precise but slow. The otherone is fast but imprecise. There is a need of both fast and precisedetection technology for timely assessment of biomarker level, such astroponin. The detection technology must also have low LOD and widedynamic range because of the wide distribution of biomarker levels fordifferent people.

The method and system disclosed herein aim to address this unmet need.

BRIEF SUMMARY OF THE DISCLOSURE

This summary is provided to introduce, in a simplified form, a selectionof concepts that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to use as an aid indetermining the scope of the claimed subject matter.

A system for quantitative detection of biomarkers in a fluid includes afluidic microchannel including a surface partitioned into a plurality ofzones. An illumination source is positioned to illuminate themicrochannel with light that is transmitted through the plurality ofzones. A delivery device is positioned for introducing a fluidic samplecontaining one or more biomarkers to the microchannel. A sensor, coupledto the microchannel, having a surface for generating sensor signals forindividual binding events, the surface including at least two zonescoated with a first antibody, wherein the fluidic sample flows to afirst zone and then a second zone to allow the biomarkers to bind ontothe sensor surface in the first zone where a portion of biomarkers notbound in the first zone are bound to the sensor surface or antibodies inthe second zone. The microchannel may be of any useful geometric shapethat allows flow such as straight, curved or zig-zagged to maximize thechannel length.

A detector located in the system produces an optical readoutrepresenting the individual binding events of each of the individualbiomarkers to the sensor surfaces of the first zone and the second zonein real time. An image system including a data processing unit iscoupled to receive the optical signals and quantify the individualbinding events on the sensor surfaces of the first zone and the secondzone and determines the difference in the numbers of binding events onthe first and second zones.

A key enabling component of the present invention is the introduction ofdetecting of the difference in the number of binding events on two ormore zones along the fluidic channel. This difference can also beexpressed as a gradient in the number of binding events along themicrochannel. This is in contrast to prior arts, which detect anabsolute binding number on the sensor surface. The difference orgradient approach removes non-specific binding associated with variousspecies in a practical sample that interfere with the detection of aparticular disease biomarker. Another key component is real time digitalcounting of the individual binding events, which improves the accuracyand precision. It also allows optimizing the detection time for aparticular application. This is because the precision usually increaseswith detection time, so there is a compromise between the two. Priorarts teach the detection over a predefined time interval. In the presentinvention, the detection takes place in real time, so that one canadjust the detection time based on the detection results and need of aparticular application. In other words, for a highly concentratedbiomarker sample, binding of the biomarker takes place quickly, whichproduces statistically significant readings and thus allows precisedetection with a short detection time. On the other hand, for a dilutebiomarker sample, the system of the present invention can be adapted todetect the sample over a longer time until satisfactory precision isachieved. An additional enabling component is the simple optical imagingbased on light scattering and surface plasmon resonance. Traditionallight scattering imaging is noisy for detecting nanoparticles. Thepresent innovation teaches methods to minimize noise and allows imagingof single nanoparticles (each corresponds to a binding event of thebiomarker molecule to an antibody).

BRIEF DESCRIPTION OF THE DRAWINGS

While the novel features of certain embodiments of the invention are setforth with particularity in the appended claims, the invention, both asto organization and content, will be better understood and appreciated,along with other objects and features thereof, from the followingdetailed description taken in conjunction with the drawings and figures.

FIG. 1 schematically shows an overview of the detection principle in atime sequence a-d as employed in one example of the biomarker detectionapparatus.

FIG. 2 schematically illustrates a diagram of an example of a biomarkerdetection apparatus.

FIG. 3 schematically shows an illustration of an example of amicrofluidic chip as employed in the disclosed apparatus.

FIG. 4 shows an enlarged microscopic image of an example of bindingevents.

FIGS. 5A-5C show progressively processed enlarged microscopic images ofimage processing.

FIGS. 6A-6C show computer screenshots of 3 different regions of zone 1as recorded by the image system and analyzed by ImageJ software.

FIGS. 7A-7C show computer screenshots of 3 different regions of zone 2as recorded by the image system and analyzed by ImageJ software.

FIGS. 8A-8C show computer screenshots of 3 different regions of zone 3as recorded by the image system and analyzed by ImageJ software.

FIG. 9 shows a histogram is shown for an example using beads.

FIG. 10 schematically shows an example of a biomarker detectionapparatus.

FIG. 11A schematically illustrates IgG/Anti-IgG binding quantificationas represented by a typical differential plasmonic image, showing bothbinding and unbinding of gold nanoparticles.

FIG. 11B shows magnified images of binding gold nanoparticles, showinginverted contrast in the differential images.

FIG. 11C shows magnified images of unbinding gold nanoparticles, showinginverted contrast in the differential images.

FIG. 11D graphically represents binding, unbinding and net counts ofgold nanoparticles vs. time with Anti-IgG-gold nanoparticleconcentration of 50 µg/mL.

FIG. 11E graphically represents nanoparticle counts (net) vs. incubationtime at different IgG concentrations.

FIG. 11F graphically represents a standard curve of IgG detection.

FIG. 12A graphically shows gold nanoparticle counts vs. binding time atdifferent PCT concentrations.

FIG. 12B graphically shows a standard curve of PCT detection.

FIGS. 13A-13D show particle counts vs. PCT concentration at differentcounting time intervals.

FIGS. 14A-14D show particle counts vs. counting time at different PCTconcentrations.

FIG. 14E graphically illustrates time dependence of lower limit ofquantification.

FIG. 14F graphically shows time dependence of prediction accuracy forPCT time resolved digital immunoassay measurements.

FIG. 15A schematically shows a method for preparation ofcapture-antibody coated sensor surface for PCT detection.

FIG. 15B schematically shows steps for the PCT assay with TD-ELISA.

FIG. 16A schematically shows a method for implementing a particlecounting algorithm on computer including pre-processing plasmonic imagesusing K-space filtering, temporal subtraction and shot noise reduction.

FIG. 16B schematically shows a process for identifying nanoparticleswith a template-matching algorithm by detecting binding and unbindingevents.

FIG. 16C graphically shows nanoparticle counting results resulting fromcarrying out the processes shown in FIGS. 16A and 16B.

FIG. 17 shows time dependence of coefficient of variation for PCT timein the present TD-ELISA measurements

FIG. 18 shows a log-log plot of signal output showing a response of 2logs, ranging from 31.3 pg/mL to 2000 pg/mL following the instructionsof the ELISA kit.

FIG. 19 is a table of values corresponding to the plot of FIG. 18 .

FIGS. 20A-20F show standard curves of PCT detection at different timepoints.

In the drawings, identical reference numbers identify similar elementsor components. The sizes and relative positions of elements in thedrawings are not necessarily drawn to scale. For example, the shapes ofvarious elements and angles are not drawn to scale, and some of theseelements are arbitrarily enlarged and positioned to improve drawinglegibility. Further, the particular shapes of the elements as drawn, arenot intended to convey any information regarding the actual shape of theparticular elements and have been solely selected for ease ofrecognition in the drawings.

DETAILED DESCRIPTION

The following disclosure describes a system and method for detection,identification, and quantification of biomarkers in human samples.Several features of methods and systems in accordance with exampleembodiments are set forth and described in the figures. It will beappreciated that methods and systems in accordance with other exampleembodiments can include additional procedures or features different thanthose shown in the figures. Example embodiments are described hereinwith respect to systems and methods for a large view and low noiseoptical imaging system. However, it will be understood that theseexamples are for the purpose of illustrating the principles, and thatthe invention is not so limited.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense that is as “including, but not limited to.”

Reference throughout this specification to “one example” or “an exampleembodiment,” “one embodiment,” “an embodiment” or combinations and/orvariations of these terms means that a particular feature, structure orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

Definitions

Generally, as used herein, the following terms have the followingmeanings when used within the context of microarray technology:

The articles “a” or “an” and the phrase “at least one” as used hereinrefers to one or more.

As used herein, “enzyme-linked immunosorbent assay (ELISA)”, is an assaytechnique designed for detecting and quantifying substances such aspeptides, proteins, antibodies, hormones, or other biological species,such as carbohydrates.

As used herein, “filtering” or “applying a filter” has its normallyaccepted meaning as digitally filtering a signal, such as an imageeither in the spatial or frequency domain. For example, filteringincludes a neighborhood operation, in which the value of any given pixelin the output image is determined by applying some algorithm to thevalues of the pixels in the neighborhood of the corresponding inputpixel. A pixel’s neighborhood is some set of pixels, defined by theirlocations relative to that pixel. Linear filtering is filtering in whichthe value of an output pixel is a linear combination of the values ofthe pixels in the input pixel’s neighborhood.

As used herein, “plurality” is understood to mean more than one. Forexample, a plurality refers to at least two, three, four, five, ten, 25,50, 75, 100, 1,000, 10,000 or more.

“Obtaining” is understood herein as manufacturing, purchasing, orotherwise coming into possession of.

As used herein, “real time” means responding to input immediately orwithin a time period between event and response that is imperceptible toa user.

As used herein, “Surface Plasmon Resonance (SPR)” is a phenomenon thatoccurs when polarized light hits a metal film at the interface of mediawith different refractive indices.

Referring now to FIG. 1 , an overview of the detection principle isschematically shown in a time sequence a-d. At initial time a, a bodyfluid 10 containing an analyte 12 such as, for example, a targetbiomarker, is introduced to a fluidic channel 14 and flows over a sensorsurface 16 in an entry zone 19 that may optionally be coated withdetection conjugated tag antibodies 20. It is important to note that theprocess may also be carried out without conjugation as expanded uponbelow. The sensor surface 16 may be partitioned into the entry zone 19,a first zone 22 (zone 1), a second zone 24 (zone 2), and a third zone 28(zone 3). The body fluid 10 is introduced to the fluidic channel 14 by aknown delivery device (not shown). The direction of flow is in thedirection represented by the directional arrows 11.

Note that the entry zone, zone 1, zone 2, and zone 3 are located alongthe channel between the entrance and exit of the channel. They are notnecessarily physically defined regions in the channel, but are selectedas regions at selected locations along the channel by a computersoftware algorithm. In order to compare a first zone to a second zone, apair of zones are selected by a computer software algorithm. The zonesare not necessarily sequential or located adjacent to one another.

The analytes 12 form an antibody-analyte complex 30 with the conjugatedtag antibodies 14. At time sequence b, detection of the antibody-analytecomplex is made by a sensor chip (as shown in FIG. 10 ). Then, at timesequence c, the antibody-analyte complex flows into the first zone 22where the sensor surface is coated with a capture antibody 5. Binding ofthe biomarker tag 14 to the antibody is detected with single moleculeprecision to allow counting of the individual binding events in realtime with an optical imaging system. Finally, at time sequence d,remaining detection antibody 32 that is not bound to the analyte thenflows into the third zone 28 and binds to the anti-detection antibody toensure a reliable test. In some useful examples the method and systemdisclosed herein can be used for detection, identification andquantification of biomarkers, such as proteins, peptides, exosomes,hormones, neurotransmitters, metabolites and nucleic acids.

Referring now to FIG. 2 , a schematic diagram of an example of abiomarker detection system is shown. A biomarker detection system 100includes a fluidic microchannel 102 containing different regions on asurface 16. In the system as shown, antibodies 5, 7 have previously beencoated onto the surface. As described above with respect to theprinciples, an entry zone 119 is coated with the detection conjugatedtag antibody. A first zone and a second zone 120, 122 respectively maybe coated with capture antibodies 5 for testing and self-referencing. Athird zone 124 is coated with anti-detection antibody 7 for qualityreference. The dimensions of the microfluidic channel are denoted by wand h representing the width and height of the fluidic channel, and Δxrepresents the length of zone 1 along the fluidic channel.

In operation, a body fluid containing an analyte 12 is introduced to thefluidic microchannel 102 and flows over the surface 116. In one example,the surface 116 may optionally be coated with a plurality of detectionconjugated tag antibodies in order to enhance the detection signal. inthis case, a plurality of detection antibody-analyte complexes 17 areformed. Then the plurality of detection antibody-analyte complexes 17flow to a downstream portion of the sensor surface, namely first zone120, coated with a capture antibody 5. Light 132 illuminates themicrochannel 102 and the analytes within and resultant optical signalsare transmitted to the detector 150. Binding of the conjugated tagantibody biomarkers to each antibody-analyte complex 17 is detected withsingle molecule precision to allow counting of the individual bindingevent in real time with an optical imaging system 130 which is locatedto receive signals from the sensor in real time. The optical imagingsystem is coupled to transmit data to an image processing unit 131. Theremaining detection conjugated tag antibodies 107 that are not bound tothe analyte then flows into the third zone 124 and bind to theanti-detection antibody 7 and are counted by the image system as, forexample, an internal reference to ensure a reliable test.

As will be understood with reference to FIG. 1 and FIG. 2 , in oneexample detection of the biomarkers may be accomplished by detectingantibodies bound to a biomarker molecule. In another more complexexample, quantitative detection of a biomarker may be accomplished byintroducing a fluidic sample containing the biomarker and particlescoated with a second antibody to the sensor surface along themicrochannel to allow the binding of the biomarker to the first andsecond antibody. In yet another even more complex example, quantitativedetection of a biomarker may be accomplished by allowing the binding ofthe biomarker to the first antibody, and then introducing a fluidicsample containing particles coated with a second antibody and allowingbinding the particles to biomarkers that bind to the first antibody onthe sensor surface.

To expand upon the above, in one example a system for quantitativedetection of a biomarker in a fluid, includes a fluidic microchannel; asensor surface on which an antibody that can bind specifically with thebiomarker; a delivery device for introducing a fluidic sample containingthe biomarker to the sensor surface along the microchannel and allow thebinding of the biomarker to the antibody; an illumination sourcepositioned to illuminate the sensor surface; an optical imaging systemthat captures light scattered by the individual binding events of thebiomarker molecule to the antibody on the sensor surface; an imageprocessing unit to quantify the individual binding events on the sensorsurface at least two zones along the flow direction, and determines thedifference in the numbers of binding events on the first and secondzones, and a data calibration system that correlates the difference inthe number of binding events on the first and second zones to theconcentration of the biomarker.

In yet another example, in a system for quantitative detection of abiomarker in a fluid, includes a fluidic microchannel; a sensor surfaceon which a first antibody attached; a delivery device for introducing afluidic sample containing the biomarker and particles coated with asecond antibody to the sensor surface along the microchannel and allowthe binding of the biomarker to the first and second antibody; anillumination source positioned to illuminate the sensor surface; anoptical imaging system that captures light scattered by the particles;an image processing unit to count the particles in real time, anddetermines the difference in the numbers of particles on the first andsecond zones, and a data calibration system that correlates thedifference in the number of particles on the first and second zones tothe concentration of the biomarker.

In yet another example, a system for quantitative detection of abiomarker in a fluid, includes a fluidic microchannel; a sensor surfaceon which a first antibody is attached; a delivery device forsequentially introducing a fluidic sample containing the biomarker tothe sensor surface along the microchannel and allow the binding of thebiomarker to the first antibody, and then a fluidic sample containingparticles coated with a second antibody and allow binding the particlesto biomarkers that bound to the first antibody on the sensor surface; anillumination source positioned to illuminate the sensor surface; anoptical imaging system that captures light scattered by the particles;an image processing unit to count the particles in real time, anddetermines the difference in the numbers of particles on the first andsecond zones, and a data calibration system that correlates thedifference in the number of particles on the first and second zones tothe concentration of the biomarker.

Referring now to FIG. 3 , an illustration of an example of amicrofluidic chip as employed in the disclosed system is schematicallyshown. A microfluidic chip 300 includes a microfluidic channel 301 thatis bonded to a detection chip 302. As described above, the microfluidicchannel 301 includes at least 3 partition zones indicated as zone 1,zone 2, and zone 3 as designated by broken line boxes 308, 306, and 310respectively. In operation, analyte is introduced at an input port 322and flows through the microfluidic channel 2 through zones 1, 2 and 3 tooutlet port 324 in the direction indicated by directional arrow 304. Asthe analyte flows through the microfluidic channel, it is held at eachzone for a predetermined time reaction then moves to the next zone. Thebinding signal is collected by the detector which sends detected bindingsignals to an image system which includes a processor for running thesoftware analysis on the detected signals.

Referring now to FIG. 4 , an enlarged microscopic image of an example ofbinding events is shown. In one example binding events were recorded byan image system in real time in a magnification of 20x (via 40 × highnumerical aperture objective, zoom out 0.5x). The frame rate is 20 fps.

Referring now to FIGS. 5A-5C, progressively processed enlargedmicroscopic images of image processing are shown. The image processingfor one example comprises three major steps. Referring specifically toFIG. 5A, a 700 × 700 pixel image sequence 500 is first loaded intoimaging software in the image system, as for example commerciallyavailable ImageJ software. Referring specifically to FIG. 5B, theresulting image 502 of a moving average command applied to remove anybackground pattern and reduce any shot noise shown. Finally, referringspecifically to FIG. 5C, a screenshot an image 504 after a softwaretracking command is used to count the binding signal in real-time isshown. A display 510 generated by the imaging software is also shown.

Referring now to FIGS. 6A-6C, computer screenshots of 3 differentregions of zone 1 as recorded by the image system and analyzed by ImageJsoftware are shown. The count number of these region of zone 1 are asshown, namely 297, 241 and 240 respectively in screenshots 602, 604 and606 respectively. When the analyte solution flows through and stays inzone 1, the analyte concentration is high, thus the antibodies cancapture more analytes than zone 2 and zone 3 at the same time.

Referring now to FIGS. 7A-7C, computer screenshots of 3 differentregions of zone 2 as recorded by the image system and analyzed by ImageJsoftware are shown. The count number of these region of zone 2 are asshown, namely 134, 111 and 108 respectively in screenshots 702, 704 and706 respectively. After the analyte solution has flowed through andremains in zone 1, the remaining uncaptured analyte then flows into zone2 and binds to the capture antibody in zone 2. Due to analyteconsumption in zone 1, the analyte concentration in zone 2 is lower thanthe concentration at which it flows through zone 1.

Referring now to FIGS. 8A-8C, computer screenshots of 3 differentregions of zone 3 as recorded by the image system and analyzed by ImageJsoftware are shown. The count number of these region of zone 3 are asshown, namely 42, 26 and 28 respectively in screenshots 802, 804 and 806respectively. After the analyte solution has flowed through and capturedanalyte remains in zone 1 and zone 2, the remaining uncaptured analytethen flows into zone 3 and binds to the capture antibody in zone 3. Theanalyte concentration in zone 3 is lower than the concentration at whichit flows through Zone 1 and zone 2. The trend of this concentrationgradient in zone 1, 2, and 3 can be used as a self-reference as well asa calculation to evaluate non-specific adsorption.

Referring now to FIG. 9 , a histogram is shown for an example usingbeads. The histogram shows the bead count result in different parts ofthe channel (zone 1, 2, and 3). For each zone, perform a triple paralleltest (shown in the figure as blue, orange, and gray, respectively). Foreach test, three different regions of each zone were used to calculatethe average count. The number of beads counting per view reflects thebinding signal, the large number corresponds to a strong signal. Errorbars are the standard deviation over triplicate regions of each view.The concentration of PCT solution is 20 pg/mL. The results show thatthere were significant differences in the beads count results indifferent parts. The signal gradually weakens in the direction of flow,the signal near the inlet is large, and the signal near the outlet isweak.

Referring now to FIG. 10 , there is schematically shown is an example ofa biomarker detection system. A biomarker detection system 1000 includesa sample delivery module 1002, a sensor module 1004, an optical readoutmodule 1006, a signal processing module 1008, and a data calibrationsystem 1018.

Sample Delivery Module

The sample delivery module 1002 introduces a blood or other human sampleto the system. It may also contain a means to remove non-biomarkercomponents (blood cells, platelets etc.) via filters, centrifuge orgravimetric mechanisms, before delivering the sample to the system.

Sensor Module

The sensor module 1004 features a fluidic channel (as best shown in FIG.3 ) to guide the sample to flow over a sensor surface on which a captureantibody is immobilized to bind to the biomarker. As described above,the sensor surface is bound to a detector that registers binding eventsand transmits optical or optical electrical signals to the image systemrepresenting the binding events. When an analyte is introduced into thesystem, the sensor surface of the upper stream has more binding eventsthan that of the downstream surface because of the depletion of thebiomarker in the fluid associated with the binding. This creates adecreasing number density of the biomarker bound along the sensorsurface in the direction of the flow. In other words, the sensor surfacein the upper stream has more binding than that in the downstreamsurface. This decreasing number density can be described as a gradientor a difference in the binding at two locations (zone 1 and zone 2),where the fluidic sample flows over zone 1 first and then over zone 2.

The biomarker level in the body fluid is more precisely determined fromthe difference signal from the two zones than detection at a singlelocation or averaging over the entire sensor surface. The principle ofthis difference detection (internal reference) is summarized below:

We denote the counts at zone 1 and zone 2 as N₁ and N₂, respectively,each including specific binding of the biomarker (N_(spec)) andnon-specific (N_(non-spec)) binding of the second antibody-conjugatednanoparticles, as well as common noise. Common noise here is referred toas any noise appear in both zone 1 and zone2. Examples of common noiseare light source instability, temperature and mechanical drifts. So, wehave

N₁ = N_(spec,1) + N_(non-spec,1,) + common noise

N₂ = N_(spec,2) + N_(non-spec,2,) + common noise

If assuming that the non-specific adsorption is uniform along thechannel, then N_(non-spec,1)= N_(non-spec,2), and

N₁- N₂=N_(spec,1)- N_(spec, 2,)

which removes non-specific adsorption and common noise in the system. Inother words, the detection at zone 2 serves as an internal reference toremove non-specific adsorption and common noise. Note: Two zonesdescribed here serve the purpose of illustrating the invention. Analysisof binding events at multiple locations (multiple zones) or even everyposition along the channel continuously can also be used.

Referring again to FIG. 1 , one example of the internal reference, wheresample solution flows along a fluidic channel from left (entrance) toright (exit) over a sensor surface coated with a capture antibody asillustrated. Two zones are defined on the sensor surface, zone 1 andzone 2, along the flow from the upper surface to the downstream surface.The biomarker concentration from the sample as denoted as c₀, bindingaffinity of the biomarker to the capture antibody as K_(D), numberdensity of the capture antibody as b, biomarker concentration in zone 1(upper stream) as _(C1), biomarker concentration in zone 2 (lowerstream) as _(C2). For the sake of explaining the principle of the methodand system disclosed herein, the sample fluid is introduced to zone 1 toallow incubation over sufficient time so that the binding and unbindingof the biomarker to the capture antibody reach thermal equilibrium. Itis further assumed that K_(D) is much greater than c₀. Without theseassumptions, details may change but the basic conclusions remain thesame. With these two assumptions, the number density of biomarkermolecules bound on the capture antibody is simply expressed as

$n_{1} = \frac{c1}{K_{D}}b.$

Binding of biomarker to capture antibody in zone 1 leads to deplete ofbiomarker in the fluid by an amount of

$\frac{c1}{K_{D}}b( {w\Delta x} ),$

where w and h are the width and height of the fluidic channel, and Δx isthe length of zone 1 along the fluidic channel. Consequently, thebiomarker concentration in the fluid in zone 1 will drop to c1, which isdetermined by the following equation,

$\frac{c1}{K_{D}}b( {w\Delta x} ) + c1w\Delta xh = c0w\Delta xh.$

Solving equation 3 leads to

$c1 = \frac{K_{D}h}{K_{D}h + b}c0.$

N₁, the number of biomarker molecules bound to the capture antibody inzone 1 observed by the optical imaging system, is

$N_{1} = c_{1}( \frac{b}{K_{D}} )A = c0( \frac{K_{D}h}{K_{D}h + b} )\frac{b}{K_{D}}A,$

where A is the area of imaging view.

Similarly, N₂, the number of biomarker molecules bound to the captureantibody in zone 2 observed by the optical imaging system, is given by

$N_{2} = c_{2}( \frac{b}{K_{D}} )A = c_{0}( \frac{K_{D}h}{K_{D}h + b} )^{2}\frac{b}{K_{D}}A.$

The difference signal, N1—N2, is

$N_{1} - N_{2} = c_{0}( \frac{K_{D}h}{K_{D}h + b} )( {1 - \frac{K_{D}h}{K_{D}h + b}} )\frac{b}{K_{D}}A.$

Eq. 7 shows that the difference signal is indeed proportional to thebiomarker concentration (c₀) in the sample. It also shows that themaximum difference occurs when K_(D)h « b, which means that lowerchannel height (h) is and higher capture antibody concentration will be.

The analysis above assumes the length of zone 1 is the same as that ofzone 2. One can also use a large length for zone 1 to maximize binding(thus depletion in zone 1) and the difference signal. An alternativeapproach is to include an additional zone between zone 1 and zone 2.Additionally, one can maximize biomarker binding capacity by increasingthe surface area of this additional zone.

The embodiment described above flows a sample fluid to zone 1 and allowsfor an incubation period before flowing to the next zone. An improvedembodiment is to flow a fluidic sample along the channel continuouslywithout stopping. The mathematical derivation of the detection signal(N₁-N₂) will not be the same (more complex), but the basic principleremains unchanged.

Optical Readout Module

The optical readout module features a wide view and low noise imagingfor real time detecting of the binding of a second antibody preferablyconjugated on nanoparticles to the biomarker bound on the captureantibody. Two key innovative features stand out of this disclosure arewide-view detection and real time single nanoparticle counting. Weexplain each below.

The wide view imaging of single biomarker molecule detection enables lowLOD. Digital immunoassay can detect the binding of a single biomarker.However, LOD is how low a biomarker concentration in the body fluid onecan precisely determine over a given period of time. This is determinedby the binding affinity (K_(D)) of the biomarker to the captureantibody, surface density of the capture antibody (b), and area of thesensor surface that can be imaged (A), according to Eq. 5 and Eq. 6. Therelative standard error is

$\lbrack {c_{0}( \frac{K_{D}h}{K_{D}h + b} )\frac{b}{K_{D}}A} \rbrack^{- 1/2}.$

For a given biomarker concentration (c₀) and binding affinity (K_(D)),the larger the image view area (A) and capture antibody surface density(b), the more binding events can be detected and the smaller of theerror will be. However, minimizing the detection error by increasing Aand b with the previously reported method faces technical limitationsfor reasons discussed below.

One useful technique for increasing A is to decrease the magnification.This will lower its capability to resolve nanoparticles because of tworeasons:

-   First, shot noise will be greater. The shot noise is associated with    the finite number photons detected by an image sensor (CMOS or CCD).    A low magnification image captures a wide view of the sensor    surface, but each nanoparticle in the image falls on a smaller    number of pixels on the imager. Each pixel can only detect a certain    number of photons per image frame before saturation, the total    number of pixels used to detect each nanoparticle is thus limited.    The method and system disclosed herein overcomes this shot noise    limit by maximizing illumination light intensity and integrating    signals from the individual pixels over sufficient time.-   Second, low magnification usually means lower spatial resolution    (smaller numerical aperture used for low magnification imaging),    which makes it impossible to resolve nanoparticles located on the    sensor surface within the spatial resolution. Increasing the capture    antibody surface density (b) faces the same spatial resolution    limitation. For example, if b is too large, such that the average    distance between two binding sites on the sensor surface is smaller    than the optical spatial resolution, then two nanoparticles bind to    two adjacent capture antibody molecules cannot be resolved. The    method and system disclosed herein overcome this issue with a    real-time single nanoparticle detection scheme. The scheme detects    individual nanoparticle binding to the sensor surface in real time    (e.g., with a time resolution of Δt). When two nanoparticles bind to    a region within the spatial resolution one at a time, separated with    a time interval greater than Δt, it detects two binding events    without the need of spatially resolving the two nanoparticles. This    real-time detection scheme has several benefits: It increases the    dynamic range because it can detect more nanoparticles for a given    area of the sensor surface. It minimizes errors associated with two    nanoparticles closely spaced within the spatial resolution. Finally,    real-time detection also helps to shorten detection time. For    example, if the biomarker concentration is high, then the    nanoparticles binding to the sensor surface can quickly reach a    statistically sufficient number and the detection can stop promptly    to save time.

Signal Processing Module

The signal processing module comprises software with the followingon-line and off-line algorithms:

-   (1) Perform denoise on the captured images to reduce noise while    maximizing signal to ensure that single nanoparticles can be imaged.-   (2) Count the individual nanoparticles in real time.-   (3) Determine difference (or gradient) in the counts at two (or    more) locations along the fluidic channel.-   (4) Relate the difference (or gradient) to the concentration of the    biomarker.-   (5) Evaluate the statistical variability to determine if the    detection time is sufficient. For example, the relative statistical    error would be-   $\frac{1}{\sqrt{N1 - N2}}$

The error decreases with time. If this error decreases to the precisionspecified for an application, then the detection stops and aconcentration together with error is provided. Data Calibration System

The data calibration system 1018 is a computer program run by aprocessing system in the detection system and is adapted to correlatethe difference in the number of particles on the first and second zonesto the concentration of the biomarker.

DIFFERENCES OF THE DISCLOSED METHOD AND APPARATUS FROM PREVIOUS ARTSComparison with Lateral Flow Immunoassay Stripes

The lateral flow immunoassay stripes are made of paper. It includes asampling pad for introduction of a fluidic sample, which conjugates withgold nanoparticles, flows along the strip to a region with captureantibody. The binding of the biomarker conjugated gold nanoparticlesproduces a color change visible by eye. The lateral flow immunoassayoften includes an internal reference region to ensure successful test.

The method and system disclosed herein are different from the lateralflow immunoassay in several major ways. First, in contrast to thelateral flow immunoassay strips, the present invention featuresdetection of single binding events, which is digital detection, leadingto dramatically improvement in the detection limit, dynamic range andquantification capability (rather than a qualitative positive ornegative answers). Second, the detection reads and counts singlenanoparticles (rather a color change) with an imaging system andalgorithm. Third, it features internal reference enabled by detectionand comparison of binding events along the fluidic channels. Thisinternal reference or calibration is a key to achieve high precisionrequired for many acute disease diagnosis and treatment

Comparison with the Microwell-Based Digital Immunoassay

As currently known, a microwell-based digital immunoassay uses an arrayof microwell, each performs ELISA with enzymatic amplification ^([3]).For sufficient low concentration biomarker samples, each microwell haseither zero or one biomarker molecule. Enzymatic reactions in themicrowell with a single biomarker leads to a fluorescence signal, whichis detected as one.

The method and system disclosed herein have a number of majordifferences from the microwell method. For example, they do not usemicrowells nor use signal amplification based on enzymatic reactions orfluorescent detection, which thus reduces the need of fabricating anarray of microwells, and reagents for enzymatic reaction andfluorescence detection. Further, they detect the biomarker eitherdirectly or via a detection antibody-conjugated nanoparticle, ratherthan detecting signal from each microwell. If we denote the opening areaof microwell with A_(microwell), and total sensor surface area asA_(total), the dynamic range is A_(total)/A_(microwell). In the methodand system disclosed herein, the dynamic range isA_(total)/A_(nanoparticle), where A_(nanoparticle) is the area of ananoparticle (pi*D^(∧)2/4, where D is the diameter of the nanoparticle).Because of the size of the nanoparticles is much smaller than the sizeof the microwell, the method and system disclosed herein can reachsingle molecule detection capability, but with a large dynamic range.Further, the method and system disclosed herein features an internalreference to minimize errors and improve precision.

Comparison with Single Molecule Fluorescent/Flow Cytometry DigitalImmunoassay

As currently used, flow cytometry flows a sample across a laser beam,and binding of a biomarker molecule to an-antibody leads to afluorescent signal, which is detected as a biomarker ^([5]). Thedetection in this method is sequential because it detects one moleculeat a given time, which is slow. This method also lacks internalreference, which leads to precision concerns.

The method and apparatus disclosed here significantly differs from theflow cytometry-based method. For example, it images multiple bindingevents (nanoparticles) in parallel, which shorten the detection time. Asa further example, it has an internal reference using differencedetection to minimize errors.

Comparison With Nanoparticle Enhanced Reading of ELISA

Nanoparticles have been used to enhance a binding signal, but mosttechnologies detect a layer of nanoparticles, rather than singlenanoparticles ^([6-8]). Single nanoparticle detection provides improveddetection limits. To date, two types of single molecule detectionschemes have been proposed. One is based on dark field opticalmicroscopy, which detects scattered light from the individualnanoparticles. For metal nanoparticles, plasmonic resonance leads to acolor of the nanoparticles. While capable, the dark field imagingtechnology is prone to artifacts due to impurities and non-specificadsorption. Typically, high magnification is used in the dark fieldmethod for the detection of nanoparticles ^([12]). An improved method isto use nanorods instead of nanoparticles ^([6]).

These single nanoparticle tracking approaches suffer from severallimitations. (1) Two or more nanoparticles located within a distancesmaller than optical diffraction limit cannot be resolved by the staticimages, which will mistake the two or more nanoparticles as a singleparticle, and thus lead to under counting of the particles. This isespecially the case for low magnification imaging that has poor spatialresolution, and high-density nanoparticles bound to the surface. (2)Nanoparticles must be separated with a distance greater than opticalresolution, whose fundamental limit is optical diffraction. This willlimit the number density of nanoparticles that can be detected withthese technologies, which places an upper limit in the dynamic range.

The method and system disclosed herein differ from the previouslydisclosed technologies in several ways. For example, it featuresreal-time tracking of single nanoparticles, so that two binding eventswithin the spatial resolution can be detected and counted individuallyas long as they do not bind to the surface exactly at the same time. Asa further example, the presently disclosed method uses real-timetracking thus minimizing errors due to diffraction limit and alsoincreasing the maximum number of nanoparticles can be detected withinthe image view, which expands the dynamic range of the detection. As afurther example, the real-time tracking of nanoparticles also enables analgorithm that determines if sufficient number of particles is detectedto provide the precision needed for a particular application. As yet afurther example, the method and apparatus disclosed herein includes aninternal reference to improve precision by removing non-specificadsorption and by canceling out common noise in the system.

EXAMPLES

In one example carried out by the inventors, a time-resolved digitalimmunoassay (TD-ELISA) technique based on plasmonic imaging ofnanoparticles for rapid detection of biomarkers with a wide dynamicrange was reported. It will be recognized that this is one example andthe invention is not limited by any examples detailed herein. Theplasmonic imaging offers high contrast and fast imaging ofnanoparticles, allowing detection of single molecule binding on a sensorsurface via detection antibody-conjugated nanoparticles. It featuresreal-time counting of the nanoparticles as they bind to the biomarkermolecules, which provide accurate assessment of the biomarkerconcentration without the need of reaching thermal equilibrium vialengthy incubation. The real-time counting together withsuper-localization tracking of each nanoparticle allows resolving twobinding events within a distance smaller than the diffraction limit,which enhances the dynamic range and minimizes the counting error. UsingTD-ELISA, the inventors have achieved a limit of detection ~3 pg/mL,dynamic range 4-12500 pg/mL, and total detection time of 25 mins forprocalcitonin (PCT), an important biomarker for sepsis^([34]).

TD-ELISA is based counting of gold nanoparticles (GNPs) binding to thesensor surface. The higher concentration of a biomarker, the faster isthe binding of GNPs, which means shorter detection time to reach adesired precision for concentrated biomarkers. The detection precisionis given by

$1/\sqrt{N},$

, where N, the number of GNPs, increases with time. This provides apossibility to detect the biomarker from the time required to reach afixed number of GNPs (which defines the precision), rather than countingthe GNPs with a fixed time interval regardless of the biomarkerconcentration.

In TD-ELISA, binding or unbinding of individual GNP is resolved in time.The dynamic range of TD-ELISA is limited by the maximum number of GNPthat can be covered on the area imaged, which is determined by the sizeof the GNP and the view area. If the inventors suppose GNP forms amonolayer on the sensing surface, with current view area of about 80 µmx 60 µm, full coverage of 150 nm GNP lead to a maximum packing of about2.6 × 10⁵. This number is the dynamic range of TD-ELISA in equilibrium.Considering surface bounded GNP represents only fraction of themolecules of interest, the real dynamic range of molecule concentrationcould be as high as 10⁷ (see FIG. 12B, for example).

Sensitive detection of single GNPs was achieved with a plasmonic imagingplatform, where a p polarized light beam with a proper incident anglefrom a superluminescence (SLED) diode was directed onto the sensorsurface via a 60x high numerical aperture oil immersion objective toexcite plasmons on the gold surface. The scattered and reflected lightwas collected with the same objective and imaged with a CCD camera. Thetime sequence of the plasmonic images captured the binding of theindividual GNPs with a temporal resolution of 10 ms (frame rate=106fps). Each GNP was revealed as a bright spot with a parabolic patternarising from the scattering for the plasmonic wave on the sensor surfaceby the GNP inset 47, which provides high image contrast and facilitatesaccurate tracking of single GNPs ^([23,24]). Using an automated imagingprocessing algorithm, the inventors tracked the position of eachindividual GNP and counted the individual GNP’s binding to the surfaceover time, from which a standard curve of PCT was obtained forconcentration calculation.

Referring now to FIG. 11A, IgG/Anti-IgG binding quantification asrepresented by a typical differential plasmonic image, showing bothbinding and unbinding of gold nanoparticles is schematicallyillustrated. A typical differential plasmonic image 70, shows bindingand unbinding of gold nanoparticles at vertices 80, 82 each having adiameter of about 150 nm. Scale bar 72 for the image 70 is 10 µm.Binding vertices 80 may be shown as red in a color image. Unbindingvertices 82 may be shown as green in a color image.

The automated particle counting algorithm consists of following steps.First, differential images were obtained by subtracting the first imageframe from the subsequent frames to generate time sequence differentialimages. This procedure removes common noise in the optical system andprovides high contrast images of single GNPs. Second, the algorithm usesthe distinct parabolic pattern of a single GNP plasmonic image as atemplate pattern to search and identify all the GNPs on the sensorsurface with an autocorrelation pattern recognition algorithm. Becauseboth the binding and unbinding of the GNPs take place on the sensorsurface dynamically, two opposite patterns are observed for the binding(See FIG. 11B) and unbinding events (See FIG. 11C), respectively. Theformer is: GNP image - background, and the latter is: background - GNPimage, so the images of binding and unbinding events are inverted incontrast. This allows the algorithm to differentiate and track bothbinding and unbinding processes over time (See FIG. 11D). Third, randomnoise in the image sequence is reduced by performing moving average overtime. Fourth, the spatial location of each GNP was determined andtracked with a procedure described with respect to FIG. 16A and FIG. 16Bbelow. Super-resolution fluorescent microscopy was used by Gooding etal. to track binding events within a distance smaller than the opticaldiffraction limit ^([25]). This was achieved in the present work withthe dynamic tracking capability of plasmonic imaging, which can resolvemultiple binding events within an area smaller than the opticaldiffraction limit in time domain, as long as the individual GNPs do notbind to the area at the same moment (defined by the frame rate), whichimproves the counting accuracy and expands the dynamic range.

Referring now to FIG. 11B, a magnified image 74 of vertices 80illustrating binding gold nanoparticles 80 is shown as an example ofinverted contrast in the differential images.

Referring now to FIG. 11C, a magnified image 76 of vertices 82illustrating unbinding gold nanoparticles is shown as an example ofinverted contrast in the differential images.

Referring now to FIG. 11D, binding, unbinding and net counts of goldnanoparticles vs. time with Anti-IgG-gold nanoparticle concentration of50 ug/mL are graphically illustrated. Plot 78 graphs data comprisingparticle counts of gold nanoparticles against time in minutes withAnti-IgG-gold nanoparticle concentration of 50 µg/mL. Curve 94represents binding counts. Curve 92 represents unbinding counts. Curve90 represents net counts.

Referring now to FIG. 11E, nanoparticle counts (net) vs. incubation timeat different IgG concentrations is graphically represented. Plot 1100 isa graph of particle counts versus reaction time in minutes. A curverepresenting blank or absence of particles is represented by baseline1101. Curve 1102 represents particle count over time for a sample of5×10⁻⁹ µg/mL. Curve 1104 represents particle count over time for asample of 5×10⁻⁷ µg/mL. Curve 1106 represents particle count over timefor a sample of 5×10⁻⁵ µg/mL. Curve 1108 represents particle count overtime for a sample of 5×10⁻ ³ µg/mL. Curve 1110 represents particle countover time for a sample of 5×10⁻¹ µg/mL. Curve 1112 represents particlecount over time for a sample of 5× 10⁻¹ µg/mL.

Referring now to FIG. 11F, a standard curve of IgG detection is shown.Plot 1120 is a graph of particle counts against concentration of IgGµg/mL. The error bars are the standard deviation from triplicate testsis graphically represented. A curve 1122 is a mathematical fit to thedata. The dashed horizontal line 1126 is GNP counts of blank solutions.The area 1124 marks the dynamic range.

Validation of TD-ELISA With IgG/Anti-IgG Binding

To validate the capability of TD-ELISA for detecting antigen andquantifying antigen concentration, the inventors applied the techniqueto study the binding of IgG binding to anti-IgG. IgG was firstimmobilized onto the sensor surface, followed by incubation of anti-IgGconjugated with GNPs at different concentrations and binding of theanti-IgG to the IgG on the sensor surface was tracked by counting theindividual GNP binding events on the surface with a temporal resolutionof ~25 ms (frame rate of 26.6 fps). The number of GNPs counted with thealgorithm varies with concentration, but for each concentration itincreases linearly with time (FIG. 11E). This indicates that processrecorded within the time frame is far from reaching saturation of thebinding sites (IgG) on the sensor surface by the GNPs, and also far fromthermal equilibrium.

From the GNP counts obtained at 20 min, the inventors obtained astandard curve of IgG. The inventors repeated the experiment 3 times andfound variability less than 20% for each concentration. From thestandard deviation of the triplicate test, error bars were determinedand marked in FIG. 11F. The limit of detection was determined by themean concentration measured for the blank solutions plus three standarddeviations ^([38]), which is 64.6 fg/mL (or 0.43 fM, dashed line 125).The detectable dynamic range was from 1.3 pg/mL to 50 µg/mL, covering 7orders of magnitude. These results demonstrate the feasibility andperformance of TD-ELISA.

PCT Detection

Referring now to FIG. 12A, gold nanoparticle counts vs. binding time atdifferent PCT concentrations is graphically shown. For clarity, onlydata of some concentrations are plotted here. Plot 1300 is a graph ofparticle counts versus reaction time in minutes. A curve 1301 representsblank samples or absence of particles. Curve 1302 represents particlecount over time for a sample of 1.95 pg/mL. Curve 1304 representsparticle count over time for a sample of 7.8 pg/mL. Curve 1306represents particle count over time for a sample of 200 pg/mL. Curve1308 represents particle count over time for a sample of 2000 pg/mL.Curve 1310 represents particle count over time for a sample of 12,500pg/mL.

Detection of PCT was based on sandwich assay, where a sample containingPCT was introduced and incubated for 10 min to allow binding of PCT tothe capture antibody immobilized on the sensor surface. Biotinylateddetection antibody was then introduced followed by introducingstreptavidin-GNPs, which bind to the detection antibody via thebiotin-streptavidin interaction. The binding process was tracked overtime to determine the PCT concentration in each sample. FIG. 12A showsthe GNP counts for different concentrations of PCT in reagent diluentbuffer over 20 min captured with an imaging frame rate of 26.6 fps. ThePCT concentration ranges from 0 (blank) to 1.25×10⁴ pg/mL, which coversthe concentration range of sepsis patients (0-10⁴ pg/mL). The dynamicrange of the present TD-ELISA estimated from the maximum density of GNPsis significantly higher than demonstrated here.

Referring now to FIG. 12B, the standard curve of the PCT test at the20-minute time point, showing a linear response of 5 logs ranging from 4pg/mL to 1.25×10⁴ pg/mL (r-square = 0.9981) is shown. The error bars inthe standard curve are the standard deviations over triplicate tests.Plot 1320 is a graph of particle counts versus reaction time in minutes.A dashed horizontal line 1321 represents GNP counts of blank solutions.A curve 1322 is a fit to the equation shown below. The area 1324 marksthe dynamic range. The limit of detection determined by the meanconcentration measured for the blank solutions plus three times ofstandard deviation is 2.76 pg/mL, which is marked by the horizontaldashed line 1321. The limit of quantification defined by the meanconcentration measured for the blank solutions plus ten times of thestandard deviation is 4 pg/mL. This limit of detection is sufficient tocover sepsis detection, where the PCT level varies from 50 to 10,000pg/mL.

Referring now concurrently to FIGS. 13A-13D, time-resolved detection ofsingle GNPs helps improve the dynamic range and minimizing detectionerror associated with the binding of multiple GNPs to an area smallerthan the spatial resolution of optical imaging are shown. The inventorsshow below that the real-time detection also helps to optimize thedetection time required to achieve a desirable detection limit andprecision. The inventors studied the detection limit and precision bymeasuring GNP count vs. PCT concentration with GNP counting time of 1,2, 5 and 15 min, respectively. As expected, shorter GNP counting timeleads to more scattered data due to smaller number of GNPs counted, butthe GNP count is proportional to the logarithm of PCT concentration ineach case. To evaluate the detection limit and concentration precision,the inventors used a statistical model to determine 95% predictionintervals (marked by the first and second shaded regions in FIGS.13A-13D). The analysis shows that the error decreases with increasingtime, but the 5-min error is about the same as the 15-min error. Thisindicates that a 5-min GNP counting is sufficient for precise PCTquantification.

Referring specifically now to FIG. 13A, particle counts vs. PCTconcentration at a 1 minute counting time interval is shown. Plot 400graphs time resolved digital immunoassay measurements of PCT atdifferent concentrations and comparison with blank solutions for 1minute of gold nanoparticle counting time intervals. Three replicateswere carried out for each concentration and for each GNP counting time,which are shown as black dots 407. A line 412 represents blank sampleshaving no particles. The counts of measurement are proportional tologarithm of PCT concentration, where line 410 is a linear fitting tothe data, a first region 414 bracketing fitted line 410 and a secondregion 416 indicate 95% prediction interval according to the statisticmodel and measurement data. Three replicates for each concentration werecarried out.

Referring specifically now to FIG. 13B, particle counts vs. PCTconcentration at a 2 minute counting time interval is shown. Plot 419graphs time resolved digital immunoassay measurements of PCT atdifferent concentrations and comparison with blank solutions for 2minutes of gold nanoparticle counting time intervals. A line 422represents blank samples having no particles. The counts of measurementare proportional to logarithm of PCT concentration, where the line 420is a linear fitting to the data, a first region 424 bracketing fittedline 420 and a second region 426 indicate 95% prediction intervalaccording to the statistic model and measurement data. Three replicatesfor each concentration was carried out.

Referring specifically now to FIG. 13C, particle counts vs. PCTconcentration at a 5 minute counting time interval is shown. Plot 429graphs time resolved digital immunoassay measurements of PCT atdifferent concentrations and comparison with blank solutions for 5minutes of gold nanoparticle counting time intervals. A line 432represents blank samples having no particles. The counts of measurementare proportional to logarithm of PCT concentration, where the line 430is a linear fitting to the data, a first region 434 and a second region436 indicate 95% prediction interval according to the statistic modeland measurement data. Three replicates for each concentration wascarried out.

Referring specifically now to FIG. 13D, particle counts vs. PCTconcentration at a 15 minute counting time interval is shown. Plot 439graphs time resolved digital immunoassay measurements of PCT atdifferent concentrations and comparison with blank solutions for 15minutes of gold nanoparticle counting time intervals. A line 442represents blank samples having no particles. The counts of measurementare proportional to logarithm of PCT concentration, where the line 440is a linear fitting to the data, a first region 444 and a second region446 indicate 95% prediction interval according to the statistic modeland measurement data. Three replicates for each concentration wascarried out.

Referring now concurrently to FIGS. 14A-14F the inventors furtherexamined the detection limit and precision of TD-ELISA by comparing theGNP counts over time at different PCT concentrations, including blanksolutions. The data shows once again that the detection limit andprecision improve with GNP counting time. The data also confirms that 5minute detection time can lead to detection of PCT concentration of 3.9pg/mL. The inventors evaluated the limit of quantification andprediction accuracy vs. time, which shows high precision data can beobtained within 5 minutes, and longer time does not significantlyimprove the precision. Considering that the experiment involved 20 minincubation for PCT binding to the capture antibody and for detectionantibody binding to the PCT bound to the capture antibody, the totaldetection time is 25 min. The detection time achieved here comparesfavorably with other high-performance detection techniques (Table 2) andmay be further shortened by eliminating the second incubation step usingdetection-antibody conjugated GNPs. It is worth mentioning that thehigher concentration of the biomarker the shorter the overall detectiontime will be. This provides a possibility of detecting a biomarker fromthe time required to reach a fixed precision, rather than using a fixedtime interval regardless of the biomarker concentration.

Referring specifically now to FIG. 14A, there shown is a plot of timeresolved digital immunoassay measurements of PCT at concentration of 1.9pg/ml, where curve 1502 and curve 1504 are the mean values of threereplicates for PCT and blank measurements, respectively. A first shadedregion 1506 and a second shaded region 1508 mark the 95% predictioninterval.

Referring specifically now to FIG. 14B, there shown is a plot of timeresolved digital immunoassay measurements of PCT at concentration of 3.9pg/ml, where curve 1514 and curve 1512 are the mean values of threereplicates for PCT and blank measurements, respectively. A first shadedregion 1566 and a second shaded region 1518 mark the 95% predictioninterval.

Referring specifically now to FIG. 14C, there shown is a plot of timeresolved digital immunoassay measurements of PCT at concentration of31.3 pg/ml, where curve 1522 and curve 1524 are the mean values of threereplicates for PCT and blank measurements, respectively. A first shadedregion 1526 and a second shaded region 1528 mark the 95% predictioninterval. Inset 1529 is an expanded plot for time intervals of 2.5 minat 31.3 pg/mL PCT.

Referring specifically now to FIG. 14D, there shown is a plot of timeresolved digital immunoassay measurements of PCT at concentration of12500 pg/ml, where curve 1532 and curve 1534 are the mean values ofthree replicates for PCT and blank measurements, respectively. A firstshaded region 1536 and a second shaded region each mark the 95%prediction interval. Inset 1539 is an expanded plot for time intervalsof 2.5 min at 12500 pg/mL PCT.

Referring specifically now to FIG. 14E, time dependence of lower limitof quantification is graphically illustrated. Plot 1540 illustrates alower limit of quantification. Logarithmic curve 1542 plots PCT as afunction of time in minutes.

Referring specifically now to FIG. 14F, time dependence of predictionaccuracy for PCT time resolved digital immunoassay measurements isgraphically shown. Plot 1560 illustrates a prediction accuracy.Logarithmic curve 1562 plots a ratio of predicted PCT and true PCT as afunction of time in minutes. For this measurement, [PCT] true = 10pg/ml.

Materials and Methods Chemicals

The following chemicals were used in the examples described herein.N-hydroxysuccinimide (NHS), N-ethyl-N′-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), sodium acetate (NaOAc), acetic acid(AcOH), Tween 20, rabbit Immunoglobulin G (IgG) and Bovine serum albumin(BSA) were purchased from Sigma-Aldrich (St. Louis, MO). 150 nm goldnanoparticles coated with goat anti-rabbit IgG (C11-150-TGARG-50), and150 nm gold nanoparticles coated with streptavidin (C11-150-TS-DIH-50)were synthesized by Nanopartz™. Dithiolalkanearomatic-PEG3-OH(Dithiol-PEG-OH) and dithiolalkanearomatic-PEG6-COOH (Dithiol-PEG-COOH)were purchased from SensoPath Technologies (Bozeman, MT). Humanprocalcitonin (PCT) ELISA kit (CATALOG NUMBER: dy8350-05), and DuoSet®ancillary reagent kit 2 (catalog number: DY008) were purchased from R&Dsystem, USA. Human serum (from human male AB plasma, USA origin,sterile-filtered) was purchased from Sigma-Aldrich, USA. The kits andreagents mentioned above were accurately sub-packed and stored accordingto the requirement of instructions.

Plasmonic Imaging.

Plasmonic imaging was implemented on an inverted optical microscope(IX-81, Olympus, Shinjuku, Tokyo, Japan) with a 60 × high numericalaperture (NA 1.49) oil immersion objective. A collimated p-polarizedlight beam (1 mW) from a 680 nm light-emitting diode (LED) (L7868-01,Hamamatsu, Japan) was directed onto a sensor surface via the objectiveto excite surface plasmons. The plasmonic images were collected by a CCDcamera (Pike, F-032B, Allied Vision Technologies, Newburyport, MA) at aframe rage of 106 frames per second with a view area of 640 × 480 pixelsand a pixel size of 7.4 µm. The sensors were prepared by coating BK-7glass coverslips with 1.5-2 nm chromium and then followed by 47 nm gold.A Flexi-Perm silicone solution cell (SARSTED, Germany) was placed on topof the sensor to hold the solution. All the images were processed byusing a Matlab program.

Sample Preparation

50 µg/mL rabbit IgG solution was prepared by dissolving rabbit IgG inNaOAc/AcOH buffer (10 mM pH=5.0 NaOAc/AcOH). The solution was dilutedvia multiple serial dilution, 10x each, to reach differentconcentrations of rabbit IgG, from 5 × 10⁻⁹ to 50 µg/mL. PCT captureantibody (240 µg/mL) stock solution was diluted with NaOAc/AcOH buffer(10 mM pH=5.0 NaOAc/AcOH) to reach 2 µg/mL. PCT standard was dilutedwith reagent diluent (solution from DuoSet® ancillary reagent kit 2)using double ratio dilution method, and the final concentration for thestandard were adjusted to 12500, 6250, 2000, 1000, 200, 31.3, 7.83, 3.9,and 1.95 pg/mL. PCT spiked serum samples were prepared by diluting thePCT standard into human serum to reach 2000, 200, and 20 pg/mL. PCTdetection antibody (3 µg/mL) stock solution was diluted with reagentdiluent to reach a working concentration of 50.00 ng/mL. 150 nm goatanti-rabbit IgG coated gold nanoparticle solution was prepared bydiluting the stock solution 1000 times with DI water and then sonicatedfor 5 minutes. Streptavidin coated gold nanoparticles were diluted byadding 15 µL of gold nanoparticle solution into 135 µL of PBS buffer andthen sonicated for 5 min.

Sensor Surface Modification

The sensors were cleaned with deionized water (Milli-Q, MilliporeCorp.), and then ethanol, followed by hydrogen flaming. After cleaning,the sensors were immediately soaked into 1 mM of 50:1 PEG-OH/PEGCOOHmixed dithiol ethanol solution for 24 hours in the dark. The sensorswere then rinsed with deionized water and ethanol and dried withnitrogen gas. Each sensor was activated for immobilization of a receptormolecule to its surface by adding 100 µL mixed 1:1 NHS and EDC aqueoussolution (containing 100 mM NHS and 400 mM EDC), followed by gradientwashing (gradually diluted the NHS/EDC solution by DIwater).

IgG Binding to Anti-IgG

IgG was immobilized to an activated sensor surface by incubation with100 µL of rabbit IgG for incubation at room temperature for 10 min,followed by washing the surface with 250 µL of PBST buffer (0.05% Tween20 in PBS buffer, Corning Cellgro) three times. Residual activatedbinding sites were blocked with BSA by introducing 250 µL BSA solution(1 % w/v) for 5 mins and then washed with 250 µL of PBST buffer threetimes. 100 µL of 150 nm gold nanoparticles coated with goat anti-rabbitIgG was added, and the binding of the gold nanoparticles to IgG wastracked in real time.

Detection of PCT

PCT capture antibody was immobilized to an activated sensor byincubation with 100 µL PCT capture antibody solution for 10 min at roomtemperature, followed by washing with 250 µL of PBST three times.Residual activated binding sites were blocked with BSA by introducing250 µL of BSA solution (3% w/v) and then washed with 250 µL of PBSTbuffer three times. The capture antibody-functionalized sensor waseither used immediately or stored at 4° C. prior to PCT assay.

Conventional ELISA Assay for PCT

Following the manufacturer’s protocol, 100 µL capture antibody was addedto each well of a microplate. The well was sealed and incubatedovernight, and then washed with PBST buffer three times, blocked with300 µL reagent diluent, and incubated for at least 1 hour. The washingstep was repeated three times, and then 100 µL standard or sample wasadded and incubated for 2 hours, followed by repeating the washing stepthree times. 100 µL detection antibody was added and incubated for 2hours, and then washed three times. 100 µL of streptavidin-HRP was addedand incubated for 20 minutes followed by repeating the washing stepthree times. 100 µL substrate solution was added and incubated for 20minutes, and finally 50 µL stop solution was added. The optical densityat 450 nm was measured using a microplate reader.

Protocol of TD-ELISA

Referring now jointly to FIG. 15A and FIG. 15B, a protocol for IgGbinding to anti-IgG as used in the present work for PCT detection isschematically shown. The process may be initiated by obtaining agold-coated glass chip to be used as the SPR sensor chip at stage 1610.Next a PEG linker 1514 and a PEG spacer 1530 are added at stage 1612 andaffixed to the surface of the SPR sensor chip 1510 at stage 1614. Havingfunctionalized the sensor chip, a capture anybody 1512 for procalcitonin(PCT) is immobilized by the PEG linker 1514 at stage 1616. This isfollowed by washing, blocking for a first time interval and washing toremove excess antibodies to produce a structure having PEG linkers withimmobilized capture antibodies at stage 1620.

Now referring specifically to FIG. 15B, a sample including PCT 1516 isintroduced to allow binding to the capture antibody 1512 at stage 1622.After 10 minutes the chip is washed at stage 1624. In one example ascarried out by the inventors, a detection antibody, biotinylatedanti-PCT antibody 20, was then added to bind with the PCT bound to thecapture antibody and form a capture antibody-PCT-detection antibodycomplex at stage 1625. After a second similar time interval the complexis washed again at stage 1626 and a gold streptavidin coatednanoparticle (GNP) 1518 is introduced. The GNP 1518 will then bind tothe anti-PCT detection antibody 1520.

In one example, the capture antibody was pre-coated on a sensor surface(gold -coated glass slide) using the steps shown. The captureantibody-coated sensor was exposed to 100 µL standard or sample andincubated for 10 min, followed by washing with PBST buffer. 100 µLbiotinylated PCT detection antibody was then added and incubated for 10min. After washing 100 µL streptavidin-coated GNP solution (9.41 × 10⁸GNPs per well) was added (~530 streptavidin molecules are covalentlycoated on each GNP), during which plasmonic imaging was performed for 5min.

Referring now to FIG. 16A, pre-processing plasmonic images using K-spacefiltering, temporal subtraction and shot noise reduction as utilized inan automatic particle counting algorithm implemented on a computer orequivalent digital processor is shown schematically. A more detailedexample of image processing as used herein uses a plurality of rawplasmonic images 732 which are obtained over a time interval 734.Software implemented in a computer 733 applies image processing to aplurality of raw plasmonic images 732 the image processing includingK-space filtering 735 and temporal averaging 737 to remove backgroundnoise by the differential imaging technique to generate a plurality oftemporal differential images 740 from the plurality of raw plasmonicimages 732. Each temporal differential image 741 is generated from oneof the raw plasmonic images 732.

Referring now to FIG. 16B, a method for time-resolved tracking of singlenanoparticles with plasmonic imaging is schematically shown. Computer733 implements a template-matching algorithm 750. Binding and unbindingevents 752, 754, 764, 766 are correlated frame by frame 760, 762. Afterthe correlation, a double check procedure 770 including the steps ofdeleting spots that only have a lead signal, deleting repeat approachsignal at the same leave spot and finding binding that does not haveunbinding. This produced a resolved image 772 at a Time,s.

This procedure replaces the old way of manual counting of eachnanoparticle bound on the gold surface over time which is substantiallyimpossible because each test includes 30,000 to 130,000 frames ofplasmonic images, and each frame contains a large number ofnanoparticles with both binding and unbinding events taking place at thesame time. An imaging-processing algorithm was developed toautomatically count the binding and unbinding events using the stepsshown. Step 1: Background noise was removed by subtracting the previousframe from the current frame to generate differential image sequences,and short noise was reduced by maximizing the number of photons in thedifferential images. Step 2: To automatically identify and count eachnanoparticle in an image, a template was chosen from a pre-processedimage sequence and used to correlate ρ with each frame. To eliminate theinfluence of light intensity, template matching 750 was achieved bynormalizing the template and the images to be processed^([3]). Thebinding and unbinding of nanoparticles on each image frame weredetermined by finding a local maximum in the correlation image. To avoiddrift of the images, the algorithm automatically updated the templateafter a certain period of time. Occasionally, the plasmonic image of ananoparticle was detected as fluctuating (binding and unbinding). Theserare events (<0.1%) were detected but removed from counting.

Referring now to FIG. 16C, nanoparticle counting results generated fromcarrying out the processes shown in FIGS. 16A and 16B are plotted. Fromthe template-matched patterns on each image frame, nanoparticlesassociated with binding (curve 782) and unbinding (curve 780) weredetermined, from which the net number of nanoparticles bound to thesurface was counted (difference between curve 782 and curve 780).

Referring now to FIG. 17 , an evaluation of time dependence ofcoefficient of variation for PCT time in the present TD-ELISAmeasurements is presented. The dotted horizontal line 812 indicates 20%coefficient of variation. Plot 814 represents the coefficient ofvariation for the highest concentration of nanoparticles 12500 pg/ml.Plot 815 represents the coefficient of variation for the middleconcentration of nanoparticles 12500 pg/ml. Plot 818 represents thecoefficient of variation for the lowest concentration of nanoparticles,1.95 pg/ml.

Referring now to FIG. 18 , a log-log plot of signal output (r-square =0.9992 showing a response of 2 logs, range from 31.3 pg/mL to 2000 pg/mLfollowing the instructions of the ELISA kit is shown. Curve 900 is aplot of optical density versus human procalcitonin concentration inpg/ml. The standard curve 900 obtained by the conventional ELISA for PCTshows a logarithmic response with a dynamic range of 62.6 pg/mL to 2000pg/mL and a limit of detection of 31.3 pg/mL. The standard curve fromthe present plasmonic imaging-based TD-ELISA has a broader dynamic range(5 logs vs. 2 logs), a lower limit of detection (2.76 pg/mL vs. 31.3pg/mL) and a lower limit of quantitation (4 pg/mL vs. 62.6 pg/mL).

Referring now to FIG. 19 , there shown is a table of values for TD-ELISAdetection of IgG/anti-IgG binding. The standard curve of theIgG/anti-IgG binding can be fitted with an empirical equation widelyused in ELISA, which is given by

$\text{y =}A_{2} + \frac{A_{1} - A_{2}}{1 + ( \frac{X}{X_{0}} )p}$

with r-square of 0.9986.

Comparison With Conventional ELISA Method Using PCT Spiked Serum Samples

Referring now jointly to FIGS. 20A-20B, in order to investigate thecorrelation between the time-resolved digital immunoassay method withcurrent conventional ELISA methods, PCT spiked serum samples withconcentrations of 20, 200, and 2000 pg/mL were tested by both methods.The output values and recovery ratios are shown in Table 1.Time-resolved digital immunoassay results were analyzed by data from thefirst 5 minutes. The mean and standard deviation were calculated basedon the PCT standard curve at 5 minutes (see FIG. 11D). The measurementwas repeated three times. Recovery of each PCT spiked serum measurementconcentration shows the accuracy of using established standard curvesfor serum sample analysis. The results show that the serum had no effecton the accuracy of the test. Both the disclosed methods and conventionalELISA methods can accurately predict the PCT concentrations of 200 and2000 pg/mL. However, the conventional ELISA cannot predict 20 pg/mL dueto a higher detection limit. Therefore, the disclosed method has a goodapplication prospect in the serological detection of PCT.

TABLE 1 PCT spiked serum calculated respectively by the standard curveof TD-ELISA counting and conventional ELISA. PCT Concentration (pg/mL)TD-ELISA* (pg/mL) (This work) Recovery (%) Replicates Conventional ELISA(pg/mL) Recovery (% ) Replicates 20 21.4±0.4 107±2.1 3 NA NA 3 200215.7±30 95.5±15 3 197.63±7.8 98.5±3.9 3 2000 2010±284.8 100.5±14.2 31923.24±114.4 96.2±5.7 3 *The results of TD-ELISA are the mean andstandard deviation calculated based on the standard curve of timeresolved digital immunoassay measurements of PCT at 5 min (supplementaryinformation). Each measurement is replicated three times. Recovery (%)is the measured PCT concentration over the actual PCT concentration,which describes the accuracy of each technique.

Comparison of Different PCT Detection Technologies

Table 2 compares the performance of different PCT detection techniquesin terms of sample volume, incubation, total detection time, limit ofdetection, limit of quantitation, and dynamic range. The TD-ELISAplatform presented here can detect PCT in 100 µL of serum samples with adetection limit of 2.8 pg/mL and a limit of quantitation of 4 pg/mL fora total of 25 min, and dynamic range of ~5 logs. This performance isexcellent compared to other sensitive PCT detection technologies listedin the table.

TABLE 2 Comparison of different PCT detection technologies TD-ELISA(This work) Convention al ELISA ELECSYS BRAHMS PCT (Roche) QuanterixSiMoA ^([39]) SMCxPRO™ (MilliporeSigma)¹ Sample volume (µL) 100 100 30100 1.6 µL-3 µL Incubation time 10 min 2 hour 18 min 20 min 1 h Totalassay time 25 min 1 - 2 days 3 h >3 h 1 day LoD (pg/ml) 2.8 30 20 0.44Not available LoQ (pg/ml) 4 >30 60 1.23 0.05–2 Dynamic range (pg/ml)4-12500 31.3–2000 20–10000 1.23–900 > 4 logs ¹The parameters of SMCxPRO™was the theoretical value of the instrument, not specific obtained fromPCT detection.

Referring now to FIG. 20A, a curve of PCT detection at time point 18minutes is shown. The horizontal dashed line 908 shows the limit ofdetection, area 932 represents the dynamic range. Curve 920 is thefitted plot of article counts versus concentration. In each of the plotsFIGS. 20A-20F, because the PCT levels of sepsis patients are usuallylower than 10000 pg/mL, PCT concentrations higher than 12500 pg/mL werenot tested. The error bars in each plot are the standard deviation overtriplicate test.

Referring now to FIG. 20B, a curve of PCT detection at time point 15minutes is shown. The horizontal dashed line 910 shows the limit ofdetection, area 934 represents the dynamic range. Curve 922 is thefitted plot of article counts versus concentration.

Referring now to FIG. 20C, a curve of PCT detection at time point 10minutes is shown. The horizontal dashed line 912 shows the limit ofdetection, area 938 represents the dynamic range. Curve 924 is thefitted plot of article counts versus concentration.

Referring now to FIG. 20D, a curve of PCT detection at time point 5minutes is shown. The horizontal dashed line 914 shows the limit ofdetection, area 940 represents the dynamic range. Curve 944 is thefitted plot of article counts versus concentration.

Referring now to FIG. 20E, a curve of PCT detection at time point 4minutes is shown. The horizontal dashed line 916 shows the limit ofdetection, area 942 represents the dynamic range. Curve 928 is thefitted plot of article counts versus concentration.

Referring now to FIG. 20F, a curve of PCT detection at time point 3minutes is shown. The horizontal dashed line 918 shows the limit ofdetection, area 944 represents the dynamic range. Curve 930 is thefitted plot of article counts versus concentration.

As shown in the above description, the inventors have developed atime-resolved digital immunoassay by combining plasmonic imaging andautomated counting of single gold nanoparticles. The high contrastplasmonic imaging allows accurate tracking of each nanoparticle. Thiscapability, together with real-time imaging, leads to resolving multiplenanoparticle binding to an area smaller than the diffraction limit,leading to highly accurate detection of single binding events with awide dynamic range. The inventors validated the principle with IgGbinding to anti-IgG and demonstrated the performance of the immunoassayusing PCT in controlled buffer and in sera with a limit of detection of2.76 pg/mL, limit of quantification of 4 pg/mL, dynamic range of 0-10⁵pg/mL, and detection time of <25 mins for low concentration samples (afew pg/mL). the disclosed data also shows that the real-time countingcan significantly shorten the detection time for high concentrationsamples (more relevant to sepsis patients). The inventors anticipatethat this time-resolved digital immunoassay is particularly useful fordiagnosing and tracking progression of acute diseases (e.g., sepsis andcardiovascular diseases), where rapid and precise biomarkerquantification is needed.

Certain exemplary embodiments of the invention have been describedherein in considerable detail in order to comply with the PatentStatutes and to provide those skilled in the art with the informationneeded to apply the novel principles of the present invention, and toconstruct and use such exemplary and specialized components as arerequired. However, it is to be understood that the invention may becarried out by different equipment, and devices, and that variousmodifications, both as to the equipment details and operatingprocedures, may be accomplished without departing from the true spiritand scope of the present invention.

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What is claimed is:
 1. A method for quantitative detection of abiomarker in a fluidic sample flowing through a microfluidic channel andover a sensor surface coated with a first antibody, the methodcomprising: introducing a sample containing a biomarker into themicrofluidic channel to flow over the sensor surface; illuminating thesensor surface; allowing the biomarker in the sample to bind to thefirst antibody on the sensor surface; imaging light scattered by thebinding events of the biomarker to the first antibody on the sensorsurface; and employing a processor executing machine readableinstructions to perform the following: select at least two zones on thesensor surface, count a number of binding events of the biomarker to thefirst antibody in the two zones, determine a difference in the numbersof the binding events of the biomarker to the first antibody in the twozones, and determine a concentration of the biomarker from thedifference in the numbers of the binding events of the biomarker to thefirst antibody in the two zones using a calibration curve.
 2. The methodof claim 1, further comprising adding to the microfluidic channel asecond antibody conjugated with nanoparticles to bind to the biomarkersthat are bound to the first antibody.
 3. The method of claim 2, furthercomprising: filtering to reduce noise and increase signal-to-noise ratioto ensure that single nanoparticles can be imaged; counting individualnanoparticles in real time; and comparing a difference or gradient inthe numbers of binding events of the biomarker to the first antibody toa concentration of the biomarker.
 4. The method of claim 3, furthercomprising evaluating statistical variability in the numbers of bindingevents to determine sufficiency of a time of detection.
 5. The method ofclaim 2, wherein the nanoparticles comprise gold nanoparticles.
 6. Themethod of claim 1 wherein the biomarker is selected from the groupconsisting of troponins, proteins, peptides, exosomes, hormones,neurotransmitters, metabolites, and nucleic acids.
 7. A system fordetection of an analyte, comprising: a sensor having a reflective metalsurface and a plurality of capture antibodies, each antibody beingtethered by a linker, where each tethered antibody is spaced apart fromeach other tethered antibody by at least one spacer; a light sourcelocated at an incident angle with respect to the reflective metalsurface; a camera positioned to receive scattered and reflected lightfrom the sensor and configured to produce a plurality of raw plasmonicimages; an image processor coupled to the camera and configured togenerate a plurality of processed images by removing background noisefrom the plurality of raw plasmonic images using differential imagingalgorithms executed by a computer, where each processed image of theplurality of processed images is generated from a corresponding uniqueone raw plasmonic image of the plurality of raw plasmonic images; and anautomated counter coupled to the image processor and configured toperform real-time particle counting on the plurality of processed imagesto detect number of single gold nanoparticles immobilized by thetethered antibodies of the plurality of tethered antibodies.
 8. Thesystem of claim 7, wherein the gold nanoparticles comprise streptavidincoated gold nanoparticles.
 9. The system of claim 8, wherein the captureantibodies are configured to bind procalcitonin having a bound detectionantibody, and the bound detection antibody is configured to bind thestreptavidin coated gold nanoparticles.
 10. The system of claim 9,further comprising a delivery device for delivering a sample solutionincluding procalcitonin (PCT) to the sensor.
 11. The system of claim 7,wherein the linker comprises a polyethylene glycol (PEG) linker.
 12. Thesystem of claim 7, wherein the light source comprises asuperluminescence diode and an oil immersion objective.